A big-data-oriented distributed density clustering method comprises the following steps that firstly, environment virtualization is performed, and a Hadoop platform is established; secondly, data are pre-processed and loaded, wherein an original data table is extracted from a database, a needed field is intercepted through a sqoop-query command, and the pre-processed data are directly extracted to an Hdfs; thirdly, a distance matrix is calculated; fourthly, a cut-off distance and dot density are calculated; fifthly, the minimum distance between a dot and a higher-density dot is calculated; sixthly, the critical distance of a critical density point and a critical density point are determined; seventhly, dot clustering is performed, so a final clustering result is obtained; eighthly, off-group points are removed. The big-data-oriented distributed density clustering method is fast and effective when a big data set is processed, and has the effect that input parameters have good robustness on the clustering result.